数据预处理部分：
- 数据增强：torchvision中transforms模块自带功能，比较实用
- 数据预处理：torchvision中transforms也帮我们实现好了，直接调用即可
- DataLoader模块直接读取batch数据
网络模块设置：
- 加载预训练模型，torchvision中有很多经典网络架构，调用起来十分方便，并且可以用人家训练好的权重参数来继续训练，也就是所谓的迁移学习
- 需要注意的是别人训练好的任务跟咱们的可不是完全一样，需要把最后的head层改一改，一般也就是最后的全连接层，改成咱们自己的任务
- 训练时可以全部重头训练，也可以只训练最后咱们任务的层，因为前几层都是做特征提取的，本质任务目标是一致的
网络模型保存与测试
- 模型保存的时候可以带有选择性，例如在验证集中如果当前效果好则保存
- 读取模型进行实际测试
data_transforms = {
'train':
transforms.Compose([
transforms.Resize([96, 96]),
transforms.RandomRotation(45),#随机旋转，-45到45度之间随机选
transforms.CenterCrop(64),#从中心开始裁剪
transforms.RandomHorizontalFlip(p=0.5),#随机水平翻转 选择一个概率概率
transforms.RandomVerticalFlip(p=0.5),#随机垂直翻转
transforms.ColorJitter(brightness=0.2, contrast=0.1, saturation=0.1, hue=0.1),#参数1为亮度，参数2为对比度，参数3为饱和度，参数4为色相
transforms.RandomGrayscale(p=0.025),#概率转换成灰度率，3通道就是R=G=B
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])#均值，标准差
]),
'valid':
transforms.Compose([
transforms.Resize([64, 64]),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}选择性的权重更新
def set_parameter_requires_grad(model, feature_extracting):
if feature_extracting:
for param in model.parameters():
param.requires_grad = False自定义修改模型输出层,以resnet18为例
def initialize_model(model_name, num_classes, feature_extract, use_pretrained=True):
model_ft = models.resnet18(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 102)#类别数自己根据自己任务来
input_size = 64#输入大小根据自己配置来
return model_ft, input_size训练权重 选择
model_ft, input_size = initialize_model(model_name, 102, feature_extract, use_pretrained=True)
#GPU还是CPU计算
model_ft = model_ft.to(device)
# 模型保存，名字自己起
filename='checkpoint.pth'
# 是否训练所有层
params_to_update = model_ft.parameters()
print("Params to learn:")
if feature_extract:
params_to_update = []
for name,param in model_ft.named_parameters():
if param.requires_grad == True:
params_to_update.append(param)
print("\t",name)
else:
for name,param in model_ft.named_parameters():
if param.requires_grad == True:
print("\t",name)基本训练代码
def train_model(model, dataloaders, criterion, optimizer, num_epochs=25,filename='best.pt'):
#咱们要算时间的
since = time.time()
#也要记录最好的那一次
best_acc = 0
#模型也得放到你的CPU或者GPU
model.to(device)
#训练过程中打印一堆损失和指标
val_acc_history = []
train_acc_history = []
train_losses = []
valid_losses = []
#学习率
LRs = [optimizer.param_groups[0]['lr']]
#最好的那次模型，后续会变的，先初始化
best_model_wts = copy.deepcopy(model.state_dict())
#一个个epoch来遍历
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# 训练和验证
for phase in ['train', 'valid']:
if phase == 'train':
model.train() # 训练
else:
model.eval（) # 验证
running_loss = 0.0
running_corrects = 0
# 把数据都取个遍
for inputs, labels in dataloaders[phase]:
inputs = inputs.to(device)#放到你的CPU或GPU
labels = labels.to(device)
# 清零
optimizer.zero_grad()
# 只有训练的时候计算和更新梯度
outputs = model(inputs)
loss = criterion(outputs, labels)
_, preds = torch.max(outputs, 1)
# 训练阶段更新权重
if phase == 'train':
loss.backward()
optimizer.step()
# 计算损失
running_loss += loss.item() * inputs.size(0)#0表示batch那个维度
running_corrects += torch.sum(preds == labels.data)#预测结果最大的和真实值是否一致
epoch_loss = running_loss / len(dataloaders[phase].dataset)#算平均
epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)
time_elapsed = time.time() - since#一个epoch我浪费了多少时间
print('Time elapsed {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))
# 得到最好那次的模型
if phase == 'valid' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
state = {
'state_dict': model.state_dict(),#字典里key就是各层的名字，值就是训练好的权重
'best_acc': best_acc,
'optimizer' : optimizer.state_dict(),
}
torch.save(state, filename)
if phase == 'valid':
val_acc_history.append(epoch_acc)
valid_losses.append(epoch_loss)
#scheduler.step(epoch_loss)#学习率衰减
if phase == 'train':
train_acc_history.append(epoch_acc)
train_losses.append(epoch_loss)
print('Optimizer learning rate : {:.7f}'.format(optimizer.param_groups[0]['lr']))
LRs.append(optimizer.param_groups[0]['lr'])
print()
scheduler.step()#学习率衰减
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
# 训练完后用最好的一次当做模型最终的结果,等着一会测试
model.load_state_dict(best_model_wts)
return model, val_acc_history, train_acc_history, valid_losses, train_losses, LRs调用训练
model_ft, val_acc_history, train_acc_history, valid_losses, train_losses, LRs = train_model(model_ft, dataloaders, criterion, optimizer_ft, num_epochs=20)def im_convert(tensor):
""" 展示数据"""
image = tensor.to("cpu").clone().detach()
image = image.numpy().squeeze()
image = image.transpose(1,2,0)
image = image * np.array((0.229, 0.224, 0.225)) + np.array((0.485, 0.456, 0.406))
image = image.clip(0, 1)
return image
